#^takes care of this chunk
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
#^takes care of all chunks
#attach packages
library(tidyverse)
library(here)
library(sf)
library(tmap)
### install.packages('tmap')
### ^can highlight just code chunk behind #sign to run
### update.packages(ask = FALSE)
cmd-shift-enter = shortcut for running the current code chunk
cmd-option-i = short cut for making code chunk
sf_trees <- read_csv(here('data', 'sf_trees', 'sf_trees.csv'))
#show_col_types = FALSE, if you want thing below to be quiet
Example 1 Find counts of observations by legal_status & wrangle a bit
### method 1: group_by() %>% summarize()
sf_trees %>%
group_by(legal_status) %>%
summarize(tree_count = n())
## # A tibble: 10 × 2
## legal_status tree_count
## <chr> <int>
## 1 DPW Maintained 141725
## 2 Landmark tree 42
## 3 Permitted Site 39732
## 4 Planning Code 138.1 required 971
## 5 Private 163
## 6 Property Tree 316
## 7 Section 143 230
## 8 Significant Tree 1648
## 9 Undocumented 8106
## 10 <NA> 54
### method 2: different way plus a few new functions
top_5_status <- sf_trees %>%
count(legal_status) %>%
drop_na(legal_status) %>% #dropping NAs in legal status column
rename(tree_count = n) %>%
relocate(tree_count) %>% #brings to front
slice_max(tree_count, n = 5) %>% #takes top 5 but could be any number
arrange(-tree_count) #highest to lowest or desc(tree_count)
Make a graph of the top 5 from above
# put - reverses fct_reorder arrangement
ggplot(data = top_5_status, aes(x = fct_reorder(legal_status, tree_count), y = tree_count)) +
geom_col(fill = 'darkgreen') +
labs(x = 'Legal status', y = 'Tree count') +
coord_flip() +
theme_minimal()
Example 2: Only going to keep observations where legal status is “Permitted Site” and caretakes is “MTA”, and store as permitted_data_df
shift-cmd-c to comment/uncomment quickly
# sf_trees$legal_status %>% unique()
# unique(sf_trees$caretaker)
permitted_data_df <- sf_trees %>%
filter(legal_status == 'Permitted Site', caretaker == 'MTA')
# an & instead of , would do the same thing
# | is the "or" instead of the and function so permitted site OR caretake too
Example 3: Only keep Blackwood Acacia trees, and then only keep columns legal_status, data, latitude, longitude, and store as blackwood_acadia_df
blackwood_acacia_df <- sf_trees %>%
filter(str_detect(species, 'Blackwood Acacia')) %>%
select(legal_status, date, lat = latitude, lon = longitude)
### make a little graph of locations
ggplot(data = blackwood_acacia_df, aes(x = lon, y = lat)) +
geom_point(color = 'darkgreen')
Example 4: use tidyr::separate()
sf_trees_sep <- sf_trees %>%
separate(species, into = c('spp_scientific', 'spp_common'), sep = ' :: ')
Example 5: use tidyr::unite
ex_5 <- sf_trees %>%
unite('id_status', tree_id, legal_status, sep = '_COOL_')
Step 1: convert lat/lon to spatial points, st_as_sf()
blackwood_acacia_sf <- blackwood_acacia_df %>%
drop_na(lat, lon) %>%
st_as_sf(coords = c('lon', 'lat'))
### we need to tell R what the coordinate reference system is
st_crs(blackwood_acacia_sf) <- 4326
ggplot(data = blackwood_acacia_sf) +
geom_sf(color = 'darkgreen') +
theme_minimal()
Read in the SF shapefile and add to map
sf_map <- read_sf(here('data', 'sf_map', 'tl_2017_06075_roads.shp'))
sf_map_transform <- st_transform(sf_map, 4326)
ggplot(data = sf_map_transform) +
geom_sf()
Combine maps!
ggplot() +
geom_sf(data = sf_map,
size = .1,
color = 'darkgrey') +
geom_sf(data = blackwood_acacia_sf,
color = 'red',
size = 0.5) +
theme_void() +
labs(title = 'Blackwood acacias in SF')
tmap_mode('view')
tm_shape(blackwood_acacia_sf) +
tm_dots()